Heart failure is an acute and chronic disease affecting 5 million people in the United States each year. It is the leading admission diagnosis for patients 65 years of age and older and leads to 17.5 billion dollars in healthcare expenditures primarily attributable to inpatient treatment. The healthcare expenditures and emotional costs for this disease will increase in the coming decades if the elderly population increases as projected and the survival rate for ischemic heart disease continues to improve. The broad objective of this proposal is to develop an empiric clinical prediction rule that identifies patients with new onset heart failure or an acute exacerbation of chronic heart failure who are at low risk of death or nonfatal medical outcomes. The specific aims of ths project are two-fold. The first aim is to derive a prognostic rule that identifies heart failure patients at low risk of death within 30 days. The second aim is to initially validate the prognostic accuracy of the rule by testing its performance against important nonfatal medical outcomes of admission to an intensive care unit, hospital length of stay, and hospital readmission within 30 days. The prediction rule will be derived using multivariate statistical modeling to analyze a large statewide database consisting of administrative data, and key clinical findings for adult inpatients with a principal hospital discharge diagnosis of heart failure. Performance of the rule will be examined in nonfatal outcomes using cross-validation techniques. The long- term objective is to develop a quality risk stratification tool that will be used by clinicians to accurately identify low-risk heart failure patients and safely reduce the proportion of low risk patient readmissions. If this study is successful, future studies are planned to validate the heart failure clinical prediction rule prospectively in a cohort of inpatients and outpatients and to examine its safety and effectiveness in large- scale implementation trials.